packages <- c("CIMseq", "CIMseq.testing", "tidyverse", "circlize", "printr")
purrr::walk(packages, library, character.only = TRUE)
rm(packages)
##DATA
load('../data/CIMseqData.rda')
load('../data/sObj.rda')
load('../data/seuratDE.rda')
if(!dir.exists('../figures')) dir.create('../figures')
#there are 5 cells that were classified as colon but sorted as SI. These have to
#be removed manually
c <- getData(cObjSng, "classification")
s <- names(c[c %in% c("4", "9")])
i <- which(colnames(getData(cObjSng, "counts")) %in% s)
cObjSng <- CIMseqSinglets(
getData(cObjSng, "counts")[, -i],
getData(cObjSng, "counts.ercc")[, -i],
getData(cObjSng, "dim.red")[-i, ],
getData(cObjSng, "classification")[-i]
)
p <- plotUnsupervisedClass(cObjSng, cObjMul, palette('c'))
p
ggsave(
plot = p,
filename = '../figures/MGA.enge20.classes.pdf',
device = cairo_pdf,
height = 180,
width = 180,
units = "mm"
)
p <- plotUnsupervisedMarkers(
cObjSng, cObjMul,
c("Lgr5", "Ptprc", "Chga", "Dclk1", "Slc26a3", "Atoh1"),
pal = RColorBrewer::brewer.pal(8, "Set1")
)
p
ggsave(
plot = p,
filename = '../figures/MGA.enge20.markers.pdf',
device = cairo_pdf,
height = 180,
width = 180,
units = "mm"
)
p <- plotUnsupervisedMarkers(
cObjSng, cObjMul, c("Mki67"),
pal = RColorBrewer::brewer.pal(8, "Set1")
)
p
ggsave(
plot = p,
filename = '../figures/MGA.enge20.Mki67.pdf',
device = cairo_pdf,
height = 180,
width = 180,
units = "mm"
)
#Shown in differential expression analysis instead
# p <- plotUnsupervisedMarkers(
# cObjSng, cObjMul, c("Plet1"),
# pal = RColorBrewer::brewer.pal(8, "Set1")
# )
# p
# ggsave(
# plot = p,
# filename = '../figures/MGA.enge20.Plet1.pdf',
# device = cairo_pdf,
# height = 180,
# width = 180,
# units = "mm"
# )
adj <- adjustFractions(cObjSng, cObjMul, sObj)
as.data.frame(table(apply(adj, 1, sum)))
| Var1 | Freq |
|---|---|
| 0 | 142 |
| 1 | 571 |
| 2 | 588 |
| 3 | 268 |
| 4 | 94 |
| 5 | 32 |
| 6 | 5 |
| 7 | 3 |
tibble(fractions = c(fractions)) %>%
ggplot() +
geom_histogram(aes(fractions), binwidth = 0.01) +
theme_bw()
tibble(
nCellTypes = apply(adj, 1, sum),
cost = getData(sObj, "costs")
) %>%
ggplot() +
geom_boxplot(aes(nCellTypes, cost, group = nCellTypes)) +
scale_x_continuous(name = "Detected cell types", breaks = 0:max(apply(adj, 1, sum))) +
theme_bw()
tibble(
sample = names(getData(sObj, "costs")),
cost = unname(getData(sObj, "costs"))
) %>%
inner_join(
select(estimateCells(cObjSng, cObjMul), sample, estimatedCellNumber),
by = "sample"
) %>%
mutate(estimatedCellNumber = round(estimatedCellNumber)) %>%
ggplot() +
geom_boxplot(aes(estimatedCellNumber, cost, group = estimatedCellNumber)) +
scale_x_continuous(
name = "ERCC estimated cell number",
breaks = 0:max(round(pull(estimateCells(cObjSng, cObjMul), estimatedCellNumber)))
) +
theme_bw()
ercc <- filter(estimateCells(cObjSng, cObjMul), sampleType == "Multiplet")
nConnections <- apply(adj, 1, sum)
nConnections <- nConnections[match(ercc$sample, names(nConnections))]
tibble(
detectedConnections = round(nConnections),
estimatedCellNumber = round(ercc$estimatedCellNumber)
) %>%
ggplot() +
geom_boxplot(aes(estimatedCellNumber, detectedConnections, group = estimatedCellNumber)) +
scale_x_continuous(
name = "ERCC estimated cell number",
breaks = 0:max(round(ercc$estimatedCellNumber))
) +
scale_y_continuous(
name = "Detected cell number",
breaks = 0:max(round(nConnections))
) +
theme_bw()
tibble(
sample = names(nConnections),
detectedConnections = nConnections
) %>%
inner_join(tibble(
sample = colnames(getData(cObjMul, "counts")),
total.counts = colSums(getData(cObjMul, "counts"))
), by = "sample") %>%
ggplot() +
geom_boxplot(aes(detectedConnections, total.counts, group = detectedConnections)) +
scale_x_continuous(
name = "Detected cell number",
breaks = 0:max(nConnections)
) +
scale_y_continuous(name = "Total counts") +
theme_bw()
tibble(
sample = names(nConnections),
detectedConnections = nConnections
) %>%
inner_join(tibble(
sample = colnames(getData(cObjMul, "counts")),
total.ercc = colSums(getData(cObjMul, "counts.ercc"))
), by = "sample") %>%
ggplot() +
geom_boxplot(aes(detectedConnections, total.ercc, group = detectedConnections)) +
scale_x_continuous(
name = "Detected cell number",
breaks = 0:max(nConnections)
) +
scale_y_continuous(name = "Total ERCC counts") +
theme_bw()
singlets <- c(table(getData(cObjSng, "classification")))
singlets <- singlets / sum(singlets)
deconv <- colSums(adjustFractions(cObjSng, cObjMul, sObj))
deconv <- deconv[match(names(singlets), names(deconv))]
deconv <- deconv / sum(deconv)
if(!identical(names(singlets), names(deconv))) stop("name mismatch")
p <- tibble(
class = names(singlets),
singlet.freq = singlets,
multiplet.freq = deconv
) %>%
ggplot() +
geom_point(aes(singlet.freq, multiplet.freq, colour = class), size = 3) +
scale_colour_manual(values = palette('c')[order(names(palette('c')))]) +
xlim(min(c(deconv, singlets)), max(c(deconv, singlets))) +
ylim(min(c(deconv, singlets)), max(c(deconv, singlets))) +
geom_abline(slope = 1, intercept = 0, lty = 3, colour = "grey") +
labs(x = "Singlet relative frequency", y = "Multiplet relative frequency") +
guides(colour = guide_legend(title = "Cell Type")) +
theme_bw()
p
ggsave(
plot = p,
filename = '../figures/MGA.enge20.sngMulRelFreq.pdf',
device = cairo_pdf,
height = 180,
width = 180,
units = "mm"
)
plotSwarmCircos(
sObj, cObjSng, cObjMul, classOrder = classOrder.MGA('c'),
classColour = palette('c')[classOrder.MGA('c')], h.ratio = 0.85
)
## Joining, by = "class"
Only detected duplicates, triplicates, and quadruplicates.
ERCC estimated cell number set to max 4.
Weight cutoff = 10. Alpha = 1e-3.
# adj <- adjustFractions(cObjSng, cObjMul, sObj, binary = TRUE)
# samples <- rownames(adj)
# rs <- rowSums(adj)
# keep <- rs == 2 | rs == 3 | rs == 4
plotSwarmCircos(
sObj, cObjSng, cObjMul, weightCut = 10,
classOrder = classOrder.MGA('c'), theoretical.max = 4, classColour = palette('c')[classOrder.MGA('c')],
h.ratio = 0.85, alpha = 1e-3
)
## Joining, by = "class"
pdf('../figures/MGA.enge20.circos.pdf', width = 9.5, height = 9.5, onefile=FALSE)
plotSwarmCircos(
sObj, cObjSng, cObjMul, weightCut = 10,
classOrder = classOrder.MGA('c'), theoretical.max = 4, classColour = palette('c')[classOrder.MGA('c')],
h.ratio = 0.85, alpha = 1e-3
)
## Joining, by = "class"
dev.off()
## quartz_off_screen
## 2
Calculate the probability of observing Lgr5 expression when Plet1 is or is not expressed in Muc2 high expressing multiplets.
p <- getData(cObjMul, "counts.cpm") %>%
.[c("Plet1", "Lgr5", "Muc2"), ] %>%
t() %>%
matrix_to_tibble("sample") %>%
filter(Muc2 > 3000) %>% #include only Muc2 high
mutate(
express.plet1 = if_else(Plet1 > 0, TRUE, FALSE),
express.lgr5 = if_else(Lgr5 > 0, TRUE, FALSE)
) %>%
group_by(express.plet1, express.lgr5) %>%
summarize(n = n()) %>%
ungroup() %>%
group_by(express.plet1) %>%
mutate(total = sum(n)) %>%
ungroup() %>%
mutate(lgr5.prob = n / total) %>%
filter(express.lgr5) %>%
ggplot() +
geom_bar(aes(express.plet1, lgr5.prob), stat = "identity", position = position_dodge(width = 1)) +
labs(x = "Plet1 expressed", y = "Lgr5 probability") +
ggthemes::theme_few()
p
ggsave(
plot = p,
filename = '../figures/MGA.enge20.Lgr5prob.pdf',
device = cairo_pdf,
height = 180,
width = 90,
units = "mm"
)
pdata <- adjustFractions(cObjSng, cObjMul, sObj, theoretical.max = 4) %>%
matrix_to_tibble("sample") %>%
filter(`Goblet Plet1 1` == 1) %>%
select(-`Goblet Plet1 1`) %>%
gather(class, binary, -sample) %>%
group_by(sample) %>%
summarize(others = paste(class[binary == 1], collapse = ", ")) %>%
mutate(others = map(others, ~str_split(.x, ", ")[[1]])) %>%
unnest() %>%
filter(others != "") %>%
group_by(others) %>%
summarize(prob = n() / nrow(.)) %>%
rename(class = others) %>%
full_join(tibble(class = unique(getData(cObjSng, "classification")))) %>%
filter(class != "Goblet Plet1 1") %>%
replace_na(list(prob = 0))
p <- pdata %>%
ggplot() +
geom_bar(aes(class, prob), stat = "identity", position = position_dodge(width = 1)) +
geom_text(aes(class, prob + 0.01, label = round(prob, digits = 3))) +
theme_bw() +
labs(y = "Probability") +
theme(axis.title.x = element_blank(), axis.text.x = element_text(angle = 90))
p
ggsave(
plot = p,
filename = '../figures/MGA.enge20.PletIntProb.pdf',
device = cairo_pdf,
height = 240,
width = 240,
units = "mm"
)
Plot mean Lgr5 expression in Stem -> colonocyte differentiation trajectory.
classes <- c("Stem 1", "Stem 2", "Stem 3", "Transit amplifying", "Progenitor", "Colonocytes")
markers <- c("Lgr5", "Mki67", "Slc26a3")
gene.order <- markers
scale.func <- scale_radius
scale.min <- NA
scale.max <- NA
getData(cObjSng, "counts.cpm")[markers, ] %>%
t() %>%
as.data.frame() %>%
rownames_to_column("Sample") %>%
as_tibble() %>%
gather(gene, cpm, -Sample) %>%
inner_join(tibble(
Sample = colnames(getData(cObjSng, "counts")),
Classification = getData(cObjSng, "classification")
)) %>%
filter(Classification %in% classes) %>%
group_by(gene, Classification) %>%
summarize(mean = mean(cpm), pct = 100 * (length(which(cpm != 0)) / n())) %>%
mutate(scaled.mean.exp = scale(mean)) %>%
ungroup() %>%
mutate(Classification = parse_factor(Classification, levels = classes)) %>%
mutate(gene = parse_factor(gene, levels = rev(markers))) %>%
ggplot() +
geom_point(
aes(Classification, gene, size = pct, colour = scaled.mean.exp)
) +
scale_colour_gradient(low = "white", high = "darkred") +
scale.func(range = c(0, 18), limits = c(scale.min, scale.max)) +
guides(
size = guide_legend(
title = "% expressed", title.position = "top", title.hjust = 0.5
),
colour = guide_colorbar(
title = "Scaled mean expression", title.position = "top",
title.hjust = 0.5, barwidth = 10
)
) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
axis.title = element_blank(),
legend.position = "top",
legend.justification = "center"
)
## Joining, by = "Sample"
Differential expression Plet1 positive vs. Plet1 negative. Show top 25 DE genes.
data <- seuratDE %>%
rownames_to_column("gene") %>%
slice(1:25) %>%
as.data.frame()
data
| gene | p_val | avg_logFC | pct.1 | pct.2 | p_val_adj |
|---|---|---|---|---|---|
| Plet1os | 0 | 3.7486628 | 0.722 | 0.075 | 0 |
| Plet1 | 0 | 3.9084046 | 0.770 | 0.157 | 0 |
| Ddah1 | 0 | 2.1013108 | 0.827 | 0.177 | 0 |
| Car8 | 0 | 3.7732619 | 0.693 | 0.070 | 0 |
| B4galnt1 | 0 | 2.8126439 | 0.737 | 0.162 | 0 |
| B4galnt2 | 0 | 1.4412589 | 0.916 | 0.548 | 0 |
| Cgref1 | 0 | 0.9192514 | 0.955 | 0.855 | 0 |
| Fabp2 | 0 | 1.5979479 | 0.830 | 0.357 | 0 |
| Sec11c | 0 | 0.5651860 | 1.000 | 0.977 | 0 |
| Ccl9 | 0 | 2.3999766 | 0.624 | 0.125 | 0 |
| Kcnh3 | 0 | 2.7078891 | 0.516 | 0.043 | 0 |
| Muc2 | 0 | 0.7748334 | 0.994 | 0.971 | 0 |
| Isx | 0 | 4.2434932 | 0.442 | 0.014 | 0 |
| Osr2 | 0 | 4.9788936 | 0.424 | 0.020 | 0 |
| Lgals9 | 0 | 1.3359941 | 0.809 | 0.368 | 0 |
| Aqp1 | 0 | 2.7265643 | 0.534 | 0.099 | 0 |
| Mt3 | 0 | 1.8163239 | 0.621 | 0.159 | 0 |
| 2210407C18Rik | 0 | 4.2631272 | 0.394 | 0.006 | 0 |
| Ang | 0 | 1.1171340 | 0.955 | 0.730 | 0 |
| Pcsk1 | 0 | 2.9994274 | 0.445 | 0.038 | 0 |
| Retnlb | 0 | 2.7394048 | 0.594 | 0.186 | 0 |
| Rnase4 | 0 | 0.7872851 | 0.982 | 0.925 | 0 |
| Chn2 | 0 | 3.2365524 | 0.397 | 0.017 | 0 |
| Guk1 | 0 | 0.6230898 | 0.982 | 0.890 | 0 |
| Cd44 | 0 | 2.9540720 | 0.469 | 0.075 | 0 |
#plot selected
markers <- c("Plet1", "Ccl9", "Kcnh3", "Mt3", "Pcsk1", "Cgref1", "Cd44", "Lgals9", "Ang")
p <- plotUnsupervisedMarkers(cObjSng, cObjMul, markers) %>%
plotData() %>%
gather(gene, value, -Sample, -(`Sample type`:Colour)) %>%
mutate(gene = parse_factor(gene, levels = markers)) %>%
ggplot() +
geom_point(aes(`dim.red dim 1`, `dim.red dim 2`, colour = value), size = 0.5) +
scale_colour_viridis_c(option = "E") +
facet_wrap(~gene, scales = "free") +
ggthemes::theme_few() +
theme(legend.position = "top") +
guides(colour = guide_colourbar(
title = "log2(CPM + 1) normalized to [0, 1]" ,
title.position = "top",
title.hjust = 0.5,
barwidth = 15
))
p
ggsave(
plot = p,
filename = '../figures/MGA.enge20.DE.pdf',
device = cairo_pdf,
height = 240,
width = 240,
units = "mm"
)
sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] printr_0.1 circlize_0.4.8 forcats_0.4.0
## [4] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2
## [7] readr_1.3.1 tidyr_0.8.3 tibble_2.1.3
## [10] ggplot2_3.2.1 tidyverse_1.2.1 CIMseq.testing_0.0.2
## [13] CIMseq_0.3.0.2
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-141 matrixStats_0.55.0 lubridate_1.7.4
## [4] RColorBrewer_1.1-2 gmodels_2.18.1 httr_1.4.1
## [7] tools_3.6.1 backports_1.1.4 R6_2.4.0
## [10] lazyeval_0.2.2 BiocGenerics_0.30.0 colorspace_1.4-1
## [13] withr_2.1.2 tidyselect_0.2.5 gridExtra_2.3
## [16] compiler_3.6.1 cli_1.1.0 rvest_0.3.4
## [19] xml2_1.2.2 labeling_0.3 scales_1.0.0
## [22] digest_0.6.20 rmarkdown_1.15 pkgconfig_2.0.2
## [25] htmltools_0.3.6 highr_0.8 rlang_0.4.0
## [28] GlobalOptions_0.1.0 ggthemes_4.2.0 readxl_1.3.1
## [31] rstudioapi_0.10 shape_1.4.4 farver_1.1.0
## [34] generics_0.0.2 jsonlite_1.6 gtools_3.8.1
## [37] magrittr_1.5 Rcpp_1.0.2 munsell_0.5.0
## [40] S4Vectors_0.22.1 viridis_0.5.1 stringi_1.4.3
## [43] yaml_2.2.0 ggraph_2.0.0 MASS_7.3-51.4
## [46] Rtsne_0.15 grid_3.6.1 parallel_3.6.1
## [49] gdata_2.18.0 listenv_0.7.0 ggrepel_0.8.1
## [52] crayon_1.3.4 lattice_0.20-38 graphlayouts_0.5.0
## [55] haven_2.1.1 hms_0.5.1 zeallot_0.1.0
## [58] knitr_1.24 pillar_1.4.2 igraph_1.2.4.1
## [61] pso_1.0.3 future.apply_1.3.0 codetools_0.2-16
## [64] stats4_3.6.1 glue_1.3.1 evaluate_0.14
## [67] modelr_0.1.5 vctrs_0.2.0 tweenr_1.0.1
## [70] cellranger_1.1.0 gtable_0.3.0 RANN_2.6.1
## [73] polyclip_1.10-0 future_1.14.0 assertthat_0.2.1
## [76] xfun_0.9 gridBase_0.4-7 ggforce_0.3.1
## [79] broom_0.5.2 tidygraph_1.1.2 viridisLite_0.3.0
## [82] globals_0.12.4